<p>Spearman rank correlation matrix.</p>

Here I apply a Random Forest machine-learning framework, coupled with explainable AI techniques, to investigate settlement dynamics in Latium vetus (central Italy) from the Early Bronze Age to the early Iron Age (c. 2100–725 BCE). Using 108 securely dated sites and a set of environmentally constrained pseudo-absence points, I model phase-specific patterns and evaluate predictor importance through both normalized split frequencies and SHAP values, capturing structural roles as well as the magnitude and direction of effects.

The results reveal a coherent diachronic transformation in the environmental logic of settlement. Early phases are primarily structured by hydrological accessibility, whereas from MBA3 onwards topographic configuration and elevation progressively dominate, marking a shift towards morphologically distinctive and defensible locations. This transition culminates in highly canalised settlement signatures during the Final Bronze Age and RMCA phases, before the RMCAIII phase signals a landscape that is increasingly saturated, hierarchically organised and functionally diversified, despite overall political stability.